Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations2964624
Missing cells700810
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory395.8 MiB
Average record size in memory140.0 B

Variable types

Categorical4
DateTime2
Numeric12
Boolean1

Variable descriptions

object_ID{'metrics': ['missing']}
territory_ID{'metrics': ['missing', 'type']}
is_person_account{'metrics': ['missing', 'infinite']}
account_primary_country_code{'metrics': ['missing', 'distinct']}
account_country_code{'metrics': ['missing', 'unique']}
territory_name{'metrics': ['missing', 'mean']}

Alerts

store_and_fwd_flag is highly imbalanced (96.2%) Imbalance
payment_type is highly imbalanced (55.4%) Imbalance
improvement_surcharge is highly imbalanced (95.7%) Imbalance
Airport_fee is highly imbalanced (72.9%) Imbalance
passenger_count has 140162 (4.7%) missing values Missing
RatecodeID has 140162 (4.7%) missing values Missing
store_and_fwd_flag has 140162 (4.7%) missing values Missing
congestion_surcharge has 140162 (4.7%) missing values Missing
Airport_fee has 140162 (4.7%) missing values Missing
trip_distance is highly skewed (γ1 = 1001.887885) Skewed
passenger_count has 31465 (1.1%) zeros Zeros
trip_distance has 60371 (2.0%) zeros Zeros
extra has 1290548 (43.5%) zeros Zeros
mta_tax has 29707 (1.0%) zeros Zeros
tip_amount has 710292 (24.0%) zeros Zeros
tolls_amount has 2753809 (92.9%) zeros Zeros
congestion_surcharge has 217877 (7.3%) zeros Zeros

Reproduction

Analysis started2025-03-01 04:59:38.056950
Analysis finished2025-03-01 05:01:44.469952
Duration2 minutes and 6.41 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

VendorID
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.6 MiB
2
2234632 
1
729732 
6
 
260

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2964624
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 2234632
75.4%
1 729732
 
24.6%
6 260
 
< 0.1%

Length

2025-03-01T06:01:44.584290image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T06:01:44.702858image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
2 2234632
75.4%
1 729732
 
24.6%
6 260
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 2234632
75.4%
1 729732
 
24.6%
6 260
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2964624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2234632
75.4%
1 729732
 
24.6%
6 260
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2964624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2234632
75.4%
1 729732
 
24.6%
6 260
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2964624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2234632
75.4%
1 729732
 
24.6%
6 260
 
< 0.1%
Distinct1575706
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Memory size22.6 MiB
Minimum2002-12-31 22:59:39
Maximum2024-02-01 00:01:15
Invalid dates0
Invalid dates (%)0.0%
2025-03-01T06:01:44.838187image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:45.026823image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1574780
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Memory size22.6 MiB
Minimum2002-12-31 23:05:41
Maximum2024-02-02 13:56:52
Invalid dates0
Invalid dates (%)0.0%
2025-03-01T06:01:45.191745image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:45.379457image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

passenger_count
Real number (ℝ)

Missing  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing140162
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean1.3392809
Minimum0
Maximum9
Zeros31465
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size22.6 MiB
2025-03-01T06:01:45.538091image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.85028169
Coefficient of variation (CV)0.63487928
Kurtosis10.671029
Mean1.3392809
Median Absolute Deviation (MAD)0
Skewness3.0389422
Sum3782748
Variance0.72297896
MonotonicityNot monotonic
2025-03-01T06:01:45.656912image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2188739
73.8%
2 405103
 
13.7%
3 91262
 
3.1%
4 51974
 
1.8%
5 33506
 
1.1%
0 31465
 
1.1%
6 22353
 
0.8%
8 51
 
< 0.1%
7 8
 
< 0.1%
9 1
 
< 0.1%
(Missing) 140162
 
4.7%
ValueCountFrequency (%)
0 31465
 
1.1%
1 2188739
73.8%
2 405103
 
13.7%
3 91262
 
3.1%
4 51974
 
1.8%
5 33506
 
1.1%
6 22353
 
0.8%
7 8
 
< 0.1%
8 51
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 51
 
< 0.1%
7 8
 
< 0.1%
6 22353
 
0.8%
5 33506
 
1.1%
4 51974
 
1.8%
3 91262
 
3.1%
2 405103
 
13.7%
1 2188739
73.8%
0 31465
 
1.1%

trip_distance
Real number (ℝ)

Skewed  Zeros 

Distinct4489
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6521692
Minimum0
Maximum312722.3
Zeros60371
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size22.6 MiB
2025-03-01T06:01:45.836489image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.43
Q11
median1.68
Q33.11
95-th percentile13.69
Maximum312722.3
Range312722.3
Interquartile range (IQR)2.11

Descriptive statistics

Standard deviation225.46257
Coefficient of variation (CV)61.73388
Kurtosis1281274.3
Mean3.6521692
Median Absolute Deviation (MAD)0.86
Skewness1001.8879
Sum10827308
Variance50833.372
MonotonicityNot monotonic
2025-03-01T06:01:46.045178image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60371
 
2.0%
0.9 40455
 
1.4%
1 40192
 
1.4%
0.8 39964
 
1.3%
1.1 38662
 
1.3%
0.7 37603
 
1.3%
1.2 36917
 
1.2%
1.3 35131
 
1.2%
1.4 33111
 
1.1%
0.6 32791
 
1.1%
Other values (4479) 2569427
86.7%
ValueCountFrequency (%)
0 60371
2.0%
0.01 2396
 
0.1%
0.02 1652
 
0.1%
0.03 1270
 
< 0.1%
0.04 1012
 
< 0.1%
0.05 766
 
< 0.1%
0.06 662
 
< 0.1%
0.07 576
 
< 0.1%
0.08 575
 
< 0.1%
0.09 459
 
< 0.1%
ValueCountFrequency (%)
312722.3 1
< 0.1%
97793.92 1
< 0.1%
82015.45 1
< 0.1%
72975.97 1
< 0.1%
71752.26 1
< 0.1%
59282.45 1
< 0.1%
59076.43 1
< 0.1%
58298.51 1
< 0.1%
51619.36 1
< 0.1%
44018.64 1
< 0.1%

RatecodeID
Real number (ℝ)

Missing 

Distinct7
Distinct (%)< 0.1%
Missing140162
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean2.0693594
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 MiB
2025-03-01T06:01:46.208152image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum99
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.823219
Coefficient of variation (CV)4.7469854
Kurtosis93.209258
Mean2.0693594
Median Absolute Deviation (MAD)0
Skewness9.7490649
Sum5844827
Variance96.495631
MonotonicityNot monotonic
2025-03-01T06:01:46.344215image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2663350
89.8%
2 98713
 
3.3%
99 28663
 
1.0%
5 19410
 
0.7%
3 7954
 
0.3%
4 6365
 
0.2%
6 7
 
< 0.1%
(Missing) 140162
 
4.7%
ValueCountFrequency (%)
1 2663350
89.8%
2 98713
 
3.3%
3 7954
 
0.3%
4 6365
 
0.2%
5 19410
 
0.7%
6 7
 
< 0.1%
99 28663
 
1.0%
ValueCountFrequency (%)
99 28663
 
1.0%
6 7
 
< 0.1%
5 19410
 
0.7%
4 6365
 
0.2%
3 7954
 
0.3%
2 98713
 
3.3%
1 2663350
89.8%

store_and_fwd_flag
Boolean

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing140162
Missing (%)4.7%
Memory size5.7 MiB
False
2813126 
True
 
11336
(Missing)
 
140162
ValueCountFrequency (%)
False 2813126
94.9%
True 11336
 
0.4%
(Missing) 140162
 
4.7%
2025-03-01T06:01:46.447073image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

PULocationID
Real number (ℝ)

Distinct260
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.01788
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 MiB
2025-03-01T06:01:46.573714image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1132
median162
Q3234
95-th percentile249
Maximum265
Range264
Interquartile range (IQR)102

Descriptive statistics

Standard deviation63.623914
Coefficient of variation (CV)0.38323531
Kurtosis-0.82977668
Mean166.01788
Median Absolute Deviation (MAD)62
Skewness-0.27225227
Sum4.921806 × 108
Variance4048.0025
MonotonicityNot monotonic
2025-03-01T06:01:46.753031image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 145240
 
4.9%
161 143471
 
4.8%
237 142708
 
4.8%
236 136465
 
4.6%
162 106717
 
3.6%
230 106324
 
3.6%
186 104523
 
3.5%
142 104080
 
3.5%
138 89533
 
3.0%
239 88474
 
3.0%
Other values (250) 1797089
60.6%
ValueCountFrequency (%)
1 295
 
< 0.1%
2 3
 
< 0.1%
3 105
 
< 0.1%
4 3568
0.1%
6 21
 
< 0.1%
7 1811
0.1%
8 11
 
< 0.1%
9 57
 
< 0.1%
10 999
 
< 0.1%
11 58
 
< 0.1%
ValueCountFrequency (%)
265 1658
 
0.1%
264 10360
 
0.3%
263 59797
2.0%
262 42801
1.4%
261 12893
 
0.4%
260 813
 
< 0.1%
259 119
 
< 0.1%
258 185
 
< 0.1%
257 78
 
< 0.1%
256 912
 
< 0.1%

DOLocationID
Real number (ℝ)

Distinct261
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.11671
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 MiB
2025-03-01T06:01:46.934147image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43
Q1114
median162
Q3234
95-th percentile261
Maximum265
Range264
Interquartile range (IQR)120

Descriptive statistics

Standard deviation69.31535
Coefficient of variation (CV)0.41979609
Kurtosis-0.90603826
Mean165.11671
Median Absolute Deviation (MAD)68
Skewness-0.37551746
Sum4.8950897 × 108
Variance4804.6177
MonotonicityNot monotonic
2025-03-01T06:01:47.117209image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
236 142044
 
4.8%
237 130249
 
4.4%
161 111942
 
3.8%
230 90603
 
3.1%
142 89673
 
3.0%
239 89105
 
3.0%
170 86733
 
2.9%
162 85238
 
2.9%
141 83562
 
2.8%
68 74517
 
2.5%
Other values (251) 1980958
66.8%
ValueCountFrequency (%)
1 7176
0.2%
2 4
 
< 0.1%
3 247
 
< 0.1%
4 11536
0.4%
5 9
 
< 0.1%
6 62
 
< 0.1%
7 7738
0.3%
8 45
 
< 0.1%
9 284
 
< 0.1%
10 2665
 
0.1%
ValueCountFrequency (%)
265 11967
 
0.4%
264 16116
 
0.5%
263 64989
2.2%
262 48328
1.6%
261 12617
 
0.4%
260 2200
 
0.1%
259 349
 
< 0.1%
258 732
 
< 0.1%
257 1096
 
< 0.1%
256 5465
 
0.2%

payment_type
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.6 MiB
1
2319046 
2
439191 
0
 
140162
4
 
46628
3
 
19597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2964624
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2319046
78.2%
2 439191
 
14.8%
0 140162
 
4.7%
4 46628
 
1.6%
3 19597
 
0.7%

Length

2025-03-01T06:01:47.316109image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T06:01:47.409711image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
1 2319046
78.2%
2 439191
 
14.8%
0 140162
 
4.7%
4 46628
 
1.6%
3 19597
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 2319046
78.2%
2 439191
 
14.8%
0 140162
 
4.7%
4 46628
 
1.6%
3 19597
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2964624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2319046
78.2%
2 439191
 
14.8%
0 140162
 
4.7%
4 46628
 
1.6%
3 19597
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2964624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2319046
78.2%
2 439191
 
14.8%
0 140162
 
4.7%
4 46628
 
1.6%
3 19597
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2964624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2319046
78.2%
2 439191
 
14.8%
0 140162
 
4.7%
4 46628
 
1.6%
3 19597
 
0.7%

fare_amount
Real number (ℝ)

Distinct8970
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.175062
Minimum-899
Maximum5000
Zeros893
Zeros (%)< 0.1%
Negative37448
Negative (%)1.3%
Memory size22.6 MiB
2025-03-01T06:01:47.559583image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-899
5-th percentile5.1
Q18.6
median12.8
Q320.5
95-th percentile61.8
Maximum5000
Range5899
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation18.949548
Coefficient of variation (CV)1.0426126
Kurtosis3653.4671
Mean18.175062
Median Absolute Deviation (MAD)4.9
Skewness18.150372
Sum53882225
Variance359.08536
MonotonicityNot monotonic
2025-03-01T06:01:47.737878image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 140879
 
4.8%
7.9 139456
 
4.7%
9.3 138462
 
4.7%
10 135501
 
4.6%
7.2 133066
 
4.5%
10.7 127631
 
4.3%
11.4 120337
 
4.1%
6.5 118249
 
4.0%
12.1 112320
 
3.8%
12.8 103324
 
3.5%
Other values (8960) 1695399
57.2%
ValueCountFrequency (%)
-899 1
< 0.1%
-800 2
< 0.1%
-744.3 1
< 0.1%
-709 1
< 0.1%
-700 1
< 0.1%
-670 1
< 0.1%
-669.4 1
< 0.1%
-650 1
< 0.1%
-607.8 1
< 0.1%
-600 1
< 0.1%
ValueCountFrequency (%)
5000 2
< 0.1%
2500 3
< 0.1%
2221.3 1
 
< 0.1%
1616.5 1
 
< 0.1%
1000 1
 
< 0.1%
912.3 1
 
< 0.1%
899 1
 
< 0.1%
820 1
 
< 0.1%
800 2
< 0.1%
761.1 1
 
< 0.1%

extra
Real number (ℝ)

Zeros 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4515984
Minimum-7.5
Maximum14.25
Zeros1290548
Zeros (%)43.5%
Negative17548
Negative (%)0.6%
Memory size22.6 MiB
2025-03-01T06:01:47.905764image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-7.5
5-th percentile0
Q10
median1
Q32.5
95-th percentile5
Maximum14.25
Range21.75
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.8041025
Coefficient of variation (CV)1.2428385
Kurtosis2.7855932
Mean1.4515984
Median Absolute Deviation (MAD)1
Skewness1.3976617
Sum4303443.6
Variance3.2547857
MonotonicityNot monotonic
2025-03-01T06:01:48.063584image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 1290548
43.5%
2.5 705767
23.8%
1 526527
17.8%
5 192426
 
6.5%
3.5 143201
 
4.8%
6 23477
 
0.8%
7.5 22407
 
0.8%
9.25 10506
 
0.4%
-1 10287
 
0.3%
4.25 9767
 
0.3%
Other values (38) 29711
 
1.0%
ValueCountFrequency (%)
-7.5 227
 
< 0.1%
-6 319
 
< 0.1%
-5 1146
 
< 0.1%
-3.5 1
 
< 0.1%
-2.5 5564
 
0.2%
-1.5 3
 
< 0.1%
-1 10287
 
0.3%
-0.04 1
 
< 0.1%
0 1290548
43.5%
0.01 2
 
< 0.1%
ValueCountFrequency (%)
14.25 2
 
< 0.1%
12.5 1
 
< 0.1%
11.75 2440
 
0.1%
10.25 2911
 
0.1%
10 642
 
< 0.1%
9.95 1
 
< 0.1%
9.25 10506
0.4%
8.5 394
 
< 0.1%
8.2 2
 
< 0.1%
7.75 2373
 
0.1%

mta_tax
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48338231
Minimum-0.5
Maximum4
Zeros29707
Zeros (%)1.0%
Negative34434
Negative (%)1.2%
Memory size22.6 MiB
2025-03-01T06:01:48.175381image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0.5
Q10.5
median0.5
Q30.5
95-th percentile0.5
Maximum4
Range4.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11776003
Coefficient of variation (CV)0.24361676
Kurtosis57.743719
Mean0.48338231
Median Absolute Deviation (MAD)0
Skewness-7.4054623
Sum1433046.8
Variance0.013867425
MonotonicityNot monotonic
2025-03-01T06:01:48.288286image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.5 2900474
97.8%
-0.5 34434
 
1.2%
0 29707
 
1.0%
4 5
 
< 0.1%
1.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1.4 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
-0.5 34434
 
1.2%
0 29707
 
1.0%
0.5 2900474
97.8%
0.8 1
 
< 0.1%
1.4 1
 
< 0.1%
1.6 1
 
< 0.1%
3 1
 
< 0.1%
4 5
 
< 0.1%
ValueCountFrequency (%)
4 5
 
< 0.1%
3 1
 
< 0.1%
1.6 1
 
< 0.1%
1.4 1
 
< 0.1%
0.8 1
 
< 0.1%
0.5 2900474
97.8%
0 29707
 
1.0%
-0.5 34434
 
1.2%

tip_amount
Real number (ℝ)

Zeros 

Distinct4192
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.33587
Minimum-80
Maximum428
Zeros710292
Zeros (%)24.0%
Negative102
Negative (%)< 0.1%
Memory size22.6 MiB
2025-03-01T06:01:48.454285image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-80
5-th percentile0
Q11
median2.7
Q34.12
95-th percentile11.2
Maximum428
Range508
Interquartile range (IQR)3.12

Descriptive statistics

Standard deviation3.8965506
Coefficient of variation (CV)1.1680763
Kurtosis173.6392
Mean3.33587
Median Absolute Deviation (MAD)1.7
Skewness5.0541375
Sum9889600.3
Variance15.183107
MonotonicityNot monotonic
2025-03-01T06:01:48.609609image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 710292
24.0%
2 145946
 
4.9%
1 113565
 
3.8%
3 75150
 
2.5%
5 39511
 
1.3%
2.8 39085
 
1.3%
3.5 33831
 
1.1%
2.1 32854
 
1.1%
4 31961
 
1.1%
1.5 31215
 
1.1%
Other values (4182) 1711214
57.7%
ValueCountFrequency (%)
-80 1
 
< 0.1%
-66.02 1
 
< 0.1%
-65.1 1
 
< 0.1%
-52 1
 
< 0.1%
-37.58 1
 
< 0.1%
-33 1
 
< 0.1%
-22.24 1
 
< 0.1%
-22 2
< 0.1%
-17.59 1
 
< 0.1%
-16.19 3
< 0.1%
ValueCountFrequency (%)
428 1
< 0.1%
422.7 1
< 0.1%
303 1
< 0.1%
300 1
< 0.1%
280 1
< 0.1%
250 1
< 0.1%
220.88 1
< 0.1%
202 2
< 0.1%
175.17 1
< 0.1%
150 1
< 0.1%

tolls_amount
Real number (ℝ)

Zeros 

Distinct1127
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5270212
Minimum-80
Maximum115.92
Zeros2753809
Zeros (%)92.9%
Negative2035
Negative (%)0.1%
Memory size22.6 MiB
2025-03-01T06:01:48.754266image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-80
5-th percentile0
Q10
median0
Q30
95-th percentile6.94
Maximum115.92
Range195.92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1283097
Coefficient of variation (CV)4.0383758
Kurtosis72.868008
Mean0.5270212
Median Absolute Deviation (MAD)0
Skewness5.4859052
Sum1562419.7
Variance4.5297021
MonotonicityNot monotonic
2025-03-01T06:01:48.916020image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2753809
92.9%
6.94 191910
 
6.5%
13.38 2031
 
0.1%
-6.94 1685
 
0.1%
3.18 1417
 
< 0.1%
15.38 1378
 
< 0.1%
13.88 1144
 
< 0.1%
12.75 891
 
< 0.1%
14.75 574
 
< 0.1%
20.32 360
 
< 0.1%
Other values (1117) 9425
 
0.3%
ValueCountFrequency (%)
-80 1
< 0.1%
-60 1
< 0.1%
-56.64 1
< 0.1%
-55.34 1
< 0.1%
-54.02 1
< 0.1%
-52.57 1
< 0.1%
-50 2
< 0.1%
-49.26 1
< 0.1%
-48.75 1
< 0.1%
-47.26 1
< 0.1%
ValueCountFrequency (%)
115.92 1
 
< 0.1%
101.69 1
 
< 0.1%
99 1
 
< 0.1%
95.46 1
 
< 0.1%
90 1
 
< 0.1%
87 1
 
< 0.1%
85 2
 
< 0.1%
83 2
 
< 0.1%
82 1
 
< 0.1%
81 6
< 0.1%

improvement_surcharge
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.6 MiB
1.0
2927710 
-1.0
 
35500
0.0
 
838
0.3
 
574
-0.3
 
2

Length

Max length4
Median length3
Mean length3.0119752
Min length3

Characters and Unicode

Total characters8929374
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2927710
98.8%
-1.0 35500
 
1.2%
0.0 838
 
< 0.1%
0.3 574
 
< 0.1%
-0.3 2
 
< 0.1%

Length

2025-03-01T06:01:49.073932image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T06:01:49.165826image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2963210
> 99.9%
0.0 838
 
< 0.1%
0.3 576
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2965462
33.2%
. 2964624
33.2%
1 2963210
33.2%
- 35502
 
0.4%
3 576
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8929374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2965462
33.2%
. 2964624
33.2%
1 2963210
33.2%
- 35502
 
0.4%
3 576
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8929374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2965462
33.2%
. 2964624
33.2%
1 2963210
33.2%
- 35502
 
0.4%
3 576
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8929374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2965462
33.2%
. 2964624
33.2%
1 2963210
33.2%
- 35502
 
0.4%
3 576
 
< 0.1%

total_amount
Real number (ℝ)

Distinct19241
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.801505
Minimum-900
Maximum5000
Zeros416
Zeros (%)< 0.1%
Negative35504
Negative (%)1.2%
Memory size22.6 MiB
2025-03-01T06:01:49.284071image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-900
5-th percentile10.87
Q115.38
median20.1
Q328.56
95-th percentile80.19
Maximum5000
Range5900
Interquartile range (IQR)13.18

Descriptive statistics

Standard deviation23.385577
Coefficient of variation (CV)0.87254718
Kurtosis1570.4795
Mean26.801505
Median Absolute Deviation (MAD)5.8
Skewness10.68236
Sum79456384
Variance546.88523
MonotonicityNot monotonic
2025-03-01T06:01:49.435995image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.8 45432
 
1.5%
12.6 43275
 
1.5%
21 36556
 
1.2%
15.12 26687
 
0.9%
15.96 26396
 
0.9%
14.28 25970
 
0.9%
17.64 24525
 
0.8%
18.48 24349
 
0.8%
13.44 23938
 
0.8%
19.32 23514
 
0.8%
Other values (19231) 2663982
89.9%
ValueCountFrequency (%)
-900 1
< 0.1%
-801 2
< 0.1%
-753.74 1
< 0.1%
-710 1
< 0.1%
-695.75 1
< 0.1%
-671 1
< 0.1%
-652.75 1
< 0.1%
-637.87 1
< 0.1%
-591 1
< 0.1%
-578.96 1
< 0.1%
ValueCountFrequency (%)
5000 2
< 0.1%
2500 3
< 0.1%
2225.3 1
 
< 0.1%
1617.5 1
 
< 0.1%
1000 1
 
< 0.1%
940.93 1
 
< 0.1%
900 1
 
< 0.1%
821 1
 
< 0.1%
801 2
< 0.1%
775.48 1
 
< 0.1%

congestion_surcharge
Real number (ℝ)

Missing  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing140162
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean2.2561221
Minimum-2.5
Maximum2.5
Zeros217877
Zeros (%)7.3%
Negative28825
Negative (%)1.0%
Memory size22.6 MiB
2025-03-01T06:01:49.548823image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-2.5
5-th percentile0
Q12.5
median2.5
Q32.5
95-th percentile2.5
Maximum2.5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82327467
Coefficient of variation (CV)0.36490697
Kurtosis12.724867
Mean2.2561221
Median Absolute Deviation (MAD)0
Skewness-3.5314914
Sum6372331
Variance0.67778118
MonotonicityNot monotonic
2025-03-01T06:01:49.644753image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2.5 2577755
87.0%
0 217877
 
7.3%
-2.5 28824
 
1.0%
0.75 3
 
< 0.1%
1 2
 
< 0.1%
-0.75 1
 
< 0.1%
(Missing) 140162
 
4.7%
ValueCountFrequency (%)
-2.5 28824
 
1.0%
-0.75 1
 
< 0.1%
0 217877
 
7.3%
0.75 3
 
< 0.1%
1 2
 
< 0.1%
2.5 2577755
87.0%
ValueCountFrequency (%)
2.5 2577755
87.0%
1 2
 
< 0.1%
0.75 3
 
< 0.1%
0 217877
 
7.3%
-0.75 1
 
< 0.1%
-2.5 28824
 
1.0%

Airport_fee
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing140162
Missing (%)4.7%
Memory size22.6 MiB
0.0
2586789 
1.75
 
232752
-1.75
 
4921

Length

Max length5
Median length3
Mean length3.0858903
Min length3

Characters and Unicode

Total characters8715980
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2586789
87.3%
1.75 232752
 
7.9%
-1.75 4921
 
0.2%
(Missing) 140162
 
4.7%

Length

2025-03-01T06:01:49.766788image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T06:01:49.850544image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2586789
91.6%
1.75 237673
 
8.4%

Most occurring characters

ValueCountFrequency (%)
0 5173578
59.4%
. 2824462
32.4%
1 237673
 
2.7%
7 237673
 
2.7%
5 237673
 
2.7%
- 4921
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8715980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5173578
59.4%
. 2824462
32.4%
1 237673
 
2.7%
7 237673
 
2.7%
5 237673
 
2.7%
- 4921
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8715980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5173578
59.4%
. 2824462
32.4%
1 237673
 
2.7%
7 237673
 
2.7%
5 237673
 
2.7%
- 4921
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8715980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5173578
59.4%
. 2824462
32.4%
1 237673
 
2.7%
7 237673
 
2.7%
5 237673
 
2.7%
- 4921
 
0.1%

Interactions

2025-03-01T06:01:18.889952image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:11.099266image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:17.408367image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:23.461338image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:29.033444image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:34.334561image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:39.822249image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:45.589054image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:51.028820image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:57.993793image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:05.405869image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:12.477554image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:19.396821image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:11.697356image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:17.954317image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:23.933730image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:29.458649image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:34.742158image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:40.388867image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:45.980253image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:51.623933image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:58.432010image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:06.200080image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:12.936983image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:19.853942image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:12.433666image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:18.473161image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:24.413726image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:29.879542image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:35.170152image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:40.842791image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:46.497720image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:52.399122image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:58.858412image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:06.801463image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:13.380061image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:20.267894image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:12.999008image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:19.011984image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:24.867866image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:30.287682image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:35.658151image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:41.346446image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:47.029246image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:53.009064image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:59.555936image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:07.402821image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:13.828353image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:20.905937image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:13.533040image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:19.526767image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:25.513446image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:30.685192image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:36.089122image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:41.879422image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:47.469200image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:53.559450image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:00.167688image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:08.006626image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:14.287158image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:21.497339image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:14.028641image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:20.081622image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:26.014900image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:31.216836image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:36.560220image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:42.314574image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:47.893230image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:54.113488image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:00.923943image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:08.599403image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:14.937356image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:22.248541image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:14.509501image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:20.649112image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:26.519053image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:31.661849image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:37.058259image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:42.780667image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:48.296746image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:54.560411image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:01.514258image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:09.375551image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:15.402038image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:22.906006image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:15.001452image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:21.234437image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:26.939751image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:32.147411image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:37.482050image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:43.248097image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:48.692268image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:54.989750image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:02.127064image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:10.064366image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:15.862221image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:23.538673image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:15.507074image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:21.718463image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:27.350593image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:32.603815image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:37.902375image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:43.689658image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:49.086445image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:55.629182image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:02.675660image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:10.634251image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:16.369786image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:24.161632image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:15.980860image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:22.151575image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:27.765193image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:33.025315image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:38.399683image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:44.297610image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:49.511667image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:56.199044image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:03.418944image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:11.095129image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:17.135892image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:24.957506image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:16.507162image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:22.582912image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:28.187581image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:33.454925image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:38.882991image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:44.728786image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:49.936702image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:56.970379image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:04.061567image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:11.514186image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:17.701660image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:25.491646image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:16.942998image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:23.019650image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:28.590747image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:33.900411image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:39.345473image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:45.170491image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:50.549934image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:00:57.555823image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:04.774615image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:11.973442image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-01T06:01:18.342707image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Missing values

2025-03-01T06:01:25.954585image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-01T06:01:31.107786image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-01T06:01:40.868682image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.